Breast cancer is the most common cancer in women worldwide, and its early detection is critical for improving survival outcomes. As a diagnostic and screening tool, mammography can be less effective owing to the masking effect of fibroglandular tissue, but breast US has good sensitivity even in dense breasts. However, breast US is highly operator dependent, highlighting the need for artificial intelligence (AI)-driven solutions. Unlike other modalities, US is performed using a handheld device that produces a continuous real-time video stream, yielding 12000–48000 frames per examination. This can be significantly challenging for AI development and requires real-time AI inference capabilities. In this review, we classified AI solutions as computer-aided diagnosis and computer-aided detection to facilitate a functional understanding and review commercial software supported by clinical evidence.In addition, to bridge healthcare gaps and enhance patient outcomes in geographically under resourced areas, we propose a novel framework by reviewing the existing AI-based triage workflows including mobile ultrasound.

Long coronavirus disease (COVID) is a condition in which coronavirus disease 2019 (COVID-19) symptoms persist for over 3 months, and currently poses a global public health challenge. Due to varying manifestations and lack of standardized definitions, diagnostic methods, and treatments, comprehensive clinical guidelines are required. This review article, summarizing research and expert consensus up to June 2023, provides recommendations for diagnosis and long-term management of long COVID symptoms. It emphasizes thorough patient evaluation, including medical history, physical examinations, and tests, and advocates vaccination and antiviral treatments to reduce risk. Guidelines for long COVID will be updated as new knowledge emerges.

Objective: To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. Materials and Methods: This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss’ kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Results: Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%–51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34–0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Conclusion Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradientecho sequences without compromising volumetry, including lesion quantification.

Long coronavirus disease (COVID) is a condition in which coronavirus disease 2019 (COVID-19) symptoms persist for over 3 months, and currently poses a global public health challenge. Due to varying manifestations and lack of standardized definitions, diagnostic methods, and treatments, comprehensive clinical guidelines are required. This review article, summarizing research and expert consensus up to June 2023, provides recommendations for diagnosis and long-term management of long COVID symptoms. It emphasizes thorough patient evaluation, including medical history, physical examinations, and tests, and advocates vaccination and antiviral treatments to reduce risk. Guidelines for long COVID will be updated as new knowledge emerges.

Breast cancer is the most common cancer in women worldwide, and its early detection is critical for improving survival outcomes. As a diagnostic and screening tool, mammography can be less effective owing to the masking effect of fibroglandular tissue, but breast US has good sensitivity even in dense breasts. However, breast US is highly operator dependent, highlighting the need for artificial intelligence (AI)-driven solutions. Unlike other modalities, US is performed using a handheld device that produces a continuous real-time video stream, yielding 12000–48000 frames per examination. This can be significantly challenging for AI development and requires real-time AI inference capabilities. In this review, we classified AI solutions as computer-aided diagnosis and computer-aided detection to facilitate a functional understanding and review commercial software supported by clinical evidence.In addition, to bridge healthcare gaps and enhance patient outcomes in geographically under resourced areas, we propose a novel framework by reviewing the existing AI-based triage workflows including mobile ultrasound.

This study aimed to assess the utilization trends of pulmonary rehabilitation (PR) among lung cancer patients in Korea using the National Health Insurance Service (NHIS) database (2017 to 2021). PR was introduced and covered under the NHIS in 2016, primarily for chronic obstructive pulmonary disease, but recent evidence suggests its benefits for lung cancer patients. Data extraction was based on Korea Informative Classification of Diseases 8th revision codes C33 and C34, with PR prescriptions identified by codes MM440 and MM290.Descriptive statistical analysis was performed, and propensity score matching was used for comparison between PR and non-PR groups. Results showed a significant increase in PR utilization, with the number of patients receiving PR (MM440) rising from 1,002 in 2017 to 3,723 in 2021, indicating a 3.7-fold increase. However, the proportion of patients receiving PR remained low at 2.9% in 2021. Enhanced access to PR services and improved evaluation strategies are essential for optimizing patient outcomes.